CN111402585A - Detection method for sporadic congestion path - Google Patents
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Abstract
The invention provides a method for detecting sporadic congestion paths, which comprises the following steps: acquiring global positioning system GPS track data of a plurality of vehicles in a research area in a target time period; dividing a road network of a research area into a plurality of path units, and determining road direction information of each path unit according to the acquired GPS track data; determining the average speed difference of each path unit in the current time period according to the GPS track data of a plurality of vehicles in the target time period; determining candidate paths according to the topological relation of each path unit in the road network and the average speed difference of each path unit in the current time period; and judging the significance of the candidate route, and determining the candidate route as an accidental congestion route when the significance of the candidate route meets a preset significance level. The method can finish the detection of the accidental congestion path under the condition of considering the road direction, and improve the accuracy of the accidental congestion path detection.
Description
Technical Field
The invention relates to the technical field of space-time data mining and space-time statistics, in particular to a detection method for sporadic congestion paths.
Background
With the development of the urbanization process and the continuous improvement of the living standard of people, the travel demand of all people is increased, and compared with the development speed of urban road and infrastructure construction, the development speed is far delayed, and the problem of urban traffic jam is directly caused. The problem of traffic congestion in cities is mainly divided into two categories: one is the phenomenon of congestion that often occurs at similar times on close paths, called common congestion (e.g., rush hour to commute); the other is traffic jam caused by some uncontrollable factors of road traffic capacity, which is called sporadic jam (such as traffic accident). The occurrence position of the frequent congestion is relatively fixed, and the risk caused by the frequent congestion can be well controlled by timely and effective prevention. The sporadic congestion is caused by the emergency such as large performance activities or traffic accidents, has more obvious congestion characteristics compared with historical information, the occurrence time and the occurrence position of the sporadic congestion are generally unpredictable, and the sporadic congestion is lack of guidance of historical experience, so that a traffic management department needs to accurately judge the sporadic congestion, and the traffic influence caused by the sporadic congestion is avoided. Therefore, the method can be used for accurately detecting the accidental congestion to assist in explaining the cause of the accidental congestion, further strengthening urban traffic control, and maximally reducing secondary traffic accidents and providing effective help and guidance.
Currently, a series of methods have been developed for detecting occasional congestion, and according to congestion characteristics, the existing methods can be roughly divided into: (1) the method based on the speed characteristics mainly utilizes the change of the vehicle passing speed in a path to judge the accidental congestion. For example, qihongsheng et al obtains a velocity ranking distribution function of each path through mixed gaussian distribution fitting, and further identifies paths with multiple peaks and low probability of minimum peaks as sporadic congestion paths. (2) Based on time-feature-based methods, such methods interpret sporadic congestion as a path where the average transit time of the vehicle is significantly higher than the historical value. For example, Berk
The method comprises defining a likelihood ratio function according to the transit time of the vehicles on the route, and finding out the transit time of the route far greater than the historical average valueAnd the candidate regions are marked space-time regions with sporadic congestion from the candidate regions by taking the likelihood ratio function as the statistic of the significance test. (3) Methods based on traffic-occupancy characteristics define a path with low downstream traffic and low occupancy as an sporadic congested path. For example, the classical Mc-Master plots traffic-occupancy two-dimensional maps based on historical data and distinguishes sporadic congestion from other congestion types by curve fitting. (4) The method is based on comprehensive characteristics, and the method comprehensively considers a plurality of characteristics such as traffic speed, road flow and occupancy rate to identify the sporadic congestion path. For example, Qintao et al train the BP neural network algorithm using the historical classification data labeled with traffic states, establish mapping relationships between the traffic states of the paths and the traffic speeds, the traffic flows and the occupancy rates, and further determine the traffic states of the current paths.
Through the analysis, the existing method can reveal the path or the time interval where the accidental congestion occurs to a certain extent, but the influence of the directionality of the road on the excavation result is ignored, and the direction information of the road is ignored in the congestion detection process, so that the accidental congestion path on the local path is wrongly judged and missed to be judged.
Disclosure of Invention
The invention provides a method for detecting an accidental congestion path, and aims to solve the problems of misjudgment and missed judgment of the accidental congestion path on a local path caused by neglecting direction information of a road in the process of detecting the accidental congestion path.
In order to achieve the above object, an embodiment of the present invention provides a method for detecting an occasional congestion path, including:
step 1, acquiring global positioning system GPS track data of a plurality of vehicles in a research area in a target time period; the target time period comprises a current time period and a historical time period;
step 2, dividing the road network of the research area into a plurality of path units, and determining road direction information of each path unit according to the acquired GPS track data;
step 3, determining the average speed difference of each path unit in the current time period according to the GPS track data of the vehicles in the target time period;
step 4, determining candidate paths according to the topological relation of each path unit in the road network and the average speed difference of each path unit in the current time period;
and 5, judging the significance of the candidate route, and determining the candidate route as an occasional congestion route when the significance of the candidate route meets a preset significance level.
Wherein the step 2 comprises:
step 2.1, equally dividing the road network of the research area into a plurality of path units;
and 2.2, respectively aiming at each path unit, determining the driving direction of the vehicle in the current time period according to the GPS track data of any vehicle positioned on the path unit in the current time period, and determining the road direction information of the path unit according to the driving direction.
Wherein the step 3 comprises:
step 3.1, acquiring the current average speed of each path unit in the current time period;
step 3.2, acquiring the historical average speed of each path unit in the historical time period;
and 3.3, determining the average speed difference of each path unit in the current time period according to the current average speed and the historical average speed of each path unit.
Wherein the step 3.1 comprises:
step 3.11, respectively aiming at each vehicle in the plurality of vehicles, obtaining the time of the vehicle passing through each path unit in the current time period by a linear interpolation method, and obtaining the average speed of the vehicle passing through each path unit in the current time period according to the time of the vehicle passing through each path unit in the current time period;
and 3.12, respectively aiming at each path unit, determining all vehicles which pass through the path unit in the current time period from the plurality of vehicles, and taking the average value of the average speeds of all vehicles which pass through the path unit in the current time period as the current average speed of the path unit in the current time period.
Wherein the step 3.2 comprises:
step 3.21, respectively aiming at each vehicle in the plurality of vehicles, obtaining the time of the vehicle passing through each path unit in the historical time period by a linear interpolation method, and obtaining the average speed of the vehicle passing through each path unit in the historical time period according to the time of the vehicle passing through each path unit in the historical time period;
and 3.22, respectively aiming at each path unit, determining all vehicles which pass through the path unit in the historical time period from the plurality of vehicles, and taking the average value of the average speeds of all vehicles which pass through the path unit in the historical time period as the historical average speed of the path unit in the historical time period.
Wherein the step 3.3 comprises:
step 3.31, respectively aiming at each path unit, passing through a formula
Obtaining the average speed difference of the path unit in the current time period;
wherein,
representing the current average speed of the path element over said current time period,
representing the historical average speed of the path unit in the historical time period, and deltav representing the average speed difference of the path unit in the current time period.
Wherein the step 4 comprises:
step 4.1, constructing an adjacency matrix according to the topological relation of each path unit in the road network;
step 4.2, taking the path units with the average speed difference smaller than 0 in each path unit in the road network as candidate seed units;
step 4.3, respectively aiming at each candidate seed unit, executing the following steps:
expanding to the first-order neighborhood according to the topological relation of each path unit in the road network, and calculating the local G of the candidate seed unit and each adjacent path uniti *Index and select the calculated local Gi *Combining the adjacent path unit with the maximum absolute value in the exponent with the candidate seed unit until all the first-order adjacent path units are combined or the calculated local G is obtainedi *Obtaining a merging path until the absolute value of the exponent is not increased;
expanding k-order adjacent path units of the merged path according to the topological relation of each path unit in the road network until the local G of the k-order adjacent path units is calculatedi *Obtaining a candidate path until the absolute value of the index is not increased; wherein k is an integer greater than or equal to 2.
Wherein the part Gi *The formula for calculating the index is:
wherein, Δ vjRepresenting the difference between the historical average speed and the current average speed of path element j,
representing the average of all average velocity differences within said investigation region, n representing the total number of path elements within said investigation region, wi,jIs the adjacency matrix of path element i and path element j, and s is the variance of the study region.
Wherein, the calculation formula of s is as follows:
wherein, Δ vjRepresenting the difference between the historical average speed and the current average speed of path element j,
represents the average of all average velocity differences within the investigation region and n represents the total number of path elements within the investigation region.
Wherein the step 5 comprises:
step 5.1, respectively aiming at each candidate path, executing the following steps:
generating N simulation data sets for the candidate paths;
computing a likelihood ratio statistic score LL R for the candidate paths in each simulated datasetobs；
By the formula
Calculating the significance of the candidate path; wherein, # (f)i) Indicates that the condition f is satisfiediN is the total number of simulation data sets, LL RresScoring a likelihood ratio statistic for said candidate paths in the real data, SiRepresents said candidate path, p _ value (S)i) Representing the significance of the candidate path;
when the significance p _ value of the candidate path (S)i) And when the significance of the candidate route is less than or equal to α, determining that the significance of the candidate route meets a preset significance level, and determining that the candidate route is a sporadic congestion route, wherein α is the preset significance level.
The scheme of the invention has at least the following beneficial effects:
in the embodiment of the invention, a road network of a research area is divided into a plurality of path units, road direction information of each path unit and average speed difference of each path unit in a current time period are determined according to GPS track data of vehicles in the research area, then candidate paths are determined from the plurality of path units according to topological relations of the path units and the average speed difference of each path unit in the current time period, finally, the significance of the candidate paths is judged, and when the significance of the candidate paths meets a preset significance level, the candidate paths are determined to be sporadic congestion paths. Because each path unit contains the road direction information, the finally determined accidental congestion path also contains the road direction information, the detection effect of the accidental congestion path is further realized under the condition of considering the road direction, the accuracy of the accidental congestion path detection is improved, and the practicability and the reliability of the traffic management department for assisting in solving the urban traffic congestion problem are improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
Fig. 1 is a flowchart of a method for detecting an occasional congestion path according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a method for detecting an occasional congestion path, including the following steps:
step 1, acquiring global positioning system GPS track data of a plurality of vehicles in a research area in a target time period; the target time period includes a current time period and a historical time period.
In the embodiment of the present invention, the research region may be any geographic region, such as a certain market region in shenzhen city; the target time period may be any given time period (which includes time periods in which occasional congestion has occurred), such as 19:30-20:30 for each working day of month 1, 2012; the current time period is a time period in which occasional congestion occurs in the target time period, and the historical time period is other time periods except the current time period in the target time period. For example, assume that the target time period is 19:30-20:30 for each workday of month 1 of 2012, and 19:30-20:30 for day 15 of month 1 is taken as the current time period (in view of occasional congestion caused by the concert event held at 20:00 for day 15 of month 1), and 19:30-20:30 for the remaining workdays are taken as the historical time periods.
Specifically, in an embodiment of the present invention, GPS track data of a plurality of vehicles within a research area, i.e., GPS track data of each vehicle within a target time period, may be obtained by extracting GPS (global positioning system) track data from a relevant device. The vehicle can be a taxi, a bus and other vehicles of any type.
The GPS track data of the vehicle in the target time period comprises a plurality of track points of the vehicle, and abnormal points (such as the track points with the travel shorter than a preset value) in the GPS track data can be removed after the GPS track data of the vehicle in the target time period is acquired in order to improve the accuracy of detecting the accidental congestion path.
And 2, dividing the road network of the research area into a plurality of path units, and determining the road direction information of each path unit according to the acquired GPS track data.
In an embodiment of the present invention, a specific implementation manner of the step 2 includes the following steps:
and 2.1, equally dividing the road network of the research area into a plurality of path units.
In the embodiment of the present invention, after obtaining a plurality of path units, track points in the GPS track data of the vehicle obtained before are matched with the road network, so as to ensure that each counted track point can be on the road network.
And 2.2, respectively aiming at each path unit, determining the driving direction of the vehicle in the current time period according to the GPS track data of any vehicle positioned on the path unit in the current time period, and determining the road direction information of the path unit according to the driving direction.
Specifically, in the embodiment of the present invention, for each route unit, the driving direction of the vehicle in the current time period is determined according to the GPS track data (e.g. the change of the position of the longitudinal collecting point) of the same vehicle located on the route unit in the current time period, and the driving direction is used as the road direction information of the route unit.
And 3, determining the average speed difference of each path unit in the current time period according to the GPS track data of the vehicles in the target time period.
In an embodiment of the present invention, a specific implementation manner of the step 3 includes the following steps:
and 3.1, acquiring the current average speed of each path unit in the current time period.
And 3.2, acquiring the historical average speed of each path unit in the historical time period.
And 3.3, determining the average speed difference of each path unit in the current time period according to the current average speed and the historical average speed of each path unit.
Specifically, the specific implementation manner of step 3.1 includes the following steps:
and 3.11, respectively aiming at each vehicle in the plurality of vehicles, obtaining the time of the vehicle passing through each path unit in the current time period by a linear interpolation method, and obtaining the average speed of the vehicle passing through each path unit in the current time period according to the time of the vehicle passing through each path unit in the current time period.
Specifically, for each vehicle, an interpolation formula can be used
The time that the vehicle passes through each path unit in the current time period is obtained. Wherein, ti(i 1.. n.) represents the time at which the vehicle passes each original track point (i.e., the track point in the GPS track data) during the current time period, Ti(i-1, …, n) represents the time the vehicle has passed through each path unit during the current time period, li1Indicating that the vehicle is in the current time period with TiDistance of the nearest previous trace point, li2Indicating that the vehicle is in the current time period with TiThe distance of the last trace point closest in time.
It should be noted that, since the length of each route unit is clear when the road network is divided, after the time that the vehicle passes through each route unit in the current time period is obtained, the average speed of the vehicle passing through each route unit in the current time period can be obtained.
And 3.12, respectively aiming at each path unit, determining all vehicles which pass through the path unit in the current time period from the plurality of vehicles, and taking the average value of the average speeds of all vehicles which pass through the path unit in the current time period as the current average speed of the path unit in the current time period.
The specific implementation manner of the step 3.2 comprises the following steps:
and 3.21, respectively aiming at each vehicle in the plurality of vehicles, obtaining the time of the vehicle passing through each path unit in the historical time period by a linear interpolation method, and obtaining the average speed of the vehicle passing through each path unit in the historical time period according to the time of the vehicle passing through each path unit in the historical time period.
Specifically, for each vehicle, an interpolation formula can be used
The time that the vehicle passes through each path unit within the historical period of time is obtained. Wherein, ti(i 1.. n.) represents the time at which the vehicle passes each original track point (i.e., the track point in the GPS track data) over a historical period of time, Ti(i-1, …, n) represents the time the vehicle has passed through each path unit in the historical time period, li1Indicating that the vehicle has been associated with T during the historical time periodiDistance of the nearest previous trace point, li2Indicating that the vehicle has been associated with T during the historical time periodiThe distance of the last trace point closest in time.
It should be noted that, since the length of each route unit is clear when the road network is divided, after the time that the vehicle passes through each route unit in the historical time period is obtained, the average speed of the vehicle passing through each route unit in the historical time period can be obtained.
And 3.22, respectively aiming at each path unit, determining all vehicles which pass through the path unit in the historical time period from the plurality of vehicles, and taking the average value of the average speeds of all vehicles which pass through the path unit in the historical time period as the historical average speed of the path unit in the historical time period.
The specific implementation manner of the step 3.3 comprises the following steps:
step 3.31, respectively aiming at each path unit, passing through a formula
And obtaining the average speed difference of the path unit in the current time period.
Wherein,
representing the current average speed of the path element over said current time period,
representing the historical average speed of the path unit over the historical time period,Δ v represents the average speed difference of the path element over the current time period.
And 4, determining candidate paths according to the topological relation of each path unit in the road network and the average speed difference of each path unit in the current time period.
In the embodiment of the invention, the spatial local autocorrelation indexes are taken as the optimization function of the detection process of the sporadic congestion path, and the spatial local autocorrelation indexes are firstly tested one by one in the first-order neighborhood of the candidate seed unit, so that the spatial local autocorrelation index value of each expansion result is continuously increased, and then the next-order neighborhood of the merged path is expanded.
Specifically, the specific implementation manner of step 4 includes the following steps:
and 4.1, constructing an adjacency matrix according to the topological relation of each path unit in the road network.
When constructing the adjacency matrix, setting the adjacency values of the adjacent path units with topological connection as 1, and setting the rest as 0.
And 4.2, taking the path units with the average speed difference smaller than 0 in all the path units in the road network as candidate seed units.
Step 4.3, respectively aiming at each candidate seed unit, executing the following steps:
firstly, according to the topological relation of each path unit in the road network, expanding to a first-order neighborhood, and calculating the local G of the candidate seed unit and each adjacent path uniti *Index and select the calculated local Gi *Combining the adjacent path unit with the maximum absolute value in the exponent with the candidate seed unit until all the first-order adjacent path units are combined or the calculated local G is obtainedi *Obtaining a merging path until the absolute value of the exponent is not increased; then expanding k-order adjacent path units of the merged path according to the topological relation of each path unit in the road network until the local G of the k-order adjacent path units is calculatedi *Obtaining a candidate path until the absolute value of the index is not increased; wherein k is greater than or equal toAn integer equal to 2.
Wherein the above-mentioned part Gi *The formula for calculating the index is:
wherein, Δ vjRepresenting the difference between the historical average speed and the current average speed of path element j,
representing the average of all average velocity differences within said investigation region, n representing the total number of path elements within said investigation region, wi,jIs the adjacency matrix of path element i and path element j, and s is the variance of the study region. The partial G isi *The calculation formula of the index can be used to calculate each local G in step 4.3 abovei *The index, for example, when computing the local G of a candidate seed cell (denoted A) and some neighboring path cell (denoted B)i *At index, wi,jIs an adjacency matrix of a and path cells B.
Wherein, the calculation formula of s is as follows:
wherein, Δ vjRepresenting the difference between the historical average speed and the current average speed of path element j,
represents the average of all average velocity differences within the investigation region and n represents the total number of path elements within the investigation region.
And 5, judging the significance of the candidate route, and determining the candidate route as an occasional congestion route when the significance of the candidate route meets a preset significance level.
In an embodiment of the present invention, a specific implementation manner of the step 5 includes the following steps:
step 5.1, respectively aiming at each candidate path, executing the following steps:
first, N sets of simulation data are generated for the candidate paths.
Wherein each simulated data set is the average velocity of the candidate path, subject to a poisson distribution with λ being the historical average velocity.
Second, the likelihood ratio statistic score LL R for the candidate paths in each simulated dataset is calculatedobs。
Thirdly, by the formula
Calculating the significance of the candidate path; wherein, # (f)i) Indicates that the condition f is satisfiediN is the total number of simulation data sets, LL RresScoring a likelihood ratio statistic for said candidate paths in the real data, SiRepresents said candidate path, p _ value (S)i) Representing the significance of the candidate path. Wherein, when the candidate path is significant p _ value (S)i) And when the significance of the candidate route is less than or equal to α, determining that the significance of the candidate route meets a preset significance level, and determining that the candidate route is a sporadic congestion route, wherein α is the preset significance level.
Note that, the formula for calculating the likelihood ratio statistic score of the candidate route is:
wherein S isiRepresenting the candidate path, v is the average velocity of the candidate path,
for historical average speed of candidate path it is further stated that the likelihood ratio statistic score LL R for the candidate path in calculating each of the simulated data setsobsWhen the data in the data set is simulated, LL R is calculatedresThen, the actual data (as in step 3 above) is used.
It should be noted that, in the embodiment of the present invention, a road network of a research area is divided into a plurality of path units, road direction information of each path unit and an average speed difference of each path unit in a current time period are determined according to GPS track data of vehicles in the research area, then a candidate path is determined from the plurality of path units according to a topological relation of each path unit and the average speed difference of each path unit in the current time period, and finally, a saliency of the candidate path is determined, and when the saliency of the candidate path meets a preset saliency level, the candidate path is determined to be an sporadic congestion path. Because each path unit contains the road direction information, the finally determined accidental congestion path also contains the road direction information, the detection effect of the accidental congestion path is further realized under the condition of considering the road direction, the accuracy of the accidental congestion path detection is improved, and the practicability and the reliability of the traffic management department for assisting in solving the urban traffic congestion problem are improved.
Next, a specific implementation of the present invention is described by using taxi GPS track data of working day of month 1 in 2012 in a certain urban area of shenzhen city, and specific implementation steps for detecting an urban sporadic congestion path of the present invention will be specifically described with reference to this example:
1) firstly, taxi GPS track data in 19:30-20:30 working days 1 month in 2012 are extracted, data of 15 days 1 month is used as detection data of the current time period (in view of occasional congestion caused by activities of a concert held at 20:00 in the current day), data of the other working days are used as historical data, abnormal points in the GPS track data are cleared, and the extracted track points are matched with a city road network.
2) The road network in the urban area is divided into route units of 100 meters, and the directionality of the road is determined according to the traveling direction of the track on the route units.
3) And taking 10 minutes as a time interval, calculating the time for the vehicle to pass through the nodes of each route unit by using a linear interpolation method to obtain the average speed of the route units, and calculating the difference of the average speed in the current time period in each route unit.
4) Constructing an adjacency matrix according to the topological relation of each path unit, and calculating the local G of each path uniti *And the index is used for obtaining a candidate path (namely a candidate sporadic congestion path) according to a multidirectional expansion strategy.
5) In the simulation data set, the average speed of the path units in each set of simulation data set obeys Poisson distribution with lambda as historical average speed, and the likelihood ratio score of the candidate sporadic congestion path is calculated.
6) When a p value (i.e., a significance value) of the candidate sporadic congestion route is calculated (where a preset significance level is set to 0.05), if the p value of the candidate sporadic congestion route is lower than 0.05, the candidate sporadic congestion route is identified as the sporadic congestion route.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. A method for detecting sporadic congestion paths, comprising:
step 1, acquiring global positioning system GPS track data of a plurality of vehicles in a research area in a target time period; the target time period comprises a current time period and a historical time period;
step 2, dividing the road network of the research area into a plurality of path units, and determining road direction information of each path unit according to the acquired GPS track data;
step 3, determining the average speed difference of each path unit in the current time period according to the GPS track data of the vehicles in the target time period;
step 4, determining candidate paths according to the topological relation of each path unit in the road network and the average speed difference of each path unit in the current time period;
and 5, judging the significance of the candidate route, and determining the candidate route as an occasional congestion route when the significance of the candidate route meets a preset significance level.
2. The detection method according to claim 1, wherein the step 2 comprises:
step 2.1, equally dividing the road network of the research area into a plurality of path units;
and 2.2, respectively aiming at each path unit, determining the driving direction of the vehicle in the current time period according to the GPS track data of any vehicle positioned on the path unit in the current time period, and determining the road direction information of the path unit according to the driving direction.
3. The detection method according to claim 1, wherein the step 3 comprises:
step 3.1, acquiring the current average speed of each path unit in the current time period;
step 3.2, acquiring the historical average speed of each path unit in the historical time period;
and 3.3, determining the average speed difference of each path unit in the current time period according to the current average speed and the historical average speed of each path unit.
4. A detection method according to claim 3, characterised in that said step 3.1 comprises:
step 3.11, respectively aiming at each vehicle in the plurality of vehicles, obtaining the time of the vehicle passing through each path unit in the current time period by a linear interpolation method, and obtaining the average speed of the vehicle passing through each path unit in the current time period according to the time of the vehicle passing through each path unit in the current time period;
and 3.12, respectively aiming at each path unit, determining all vehicles which pass through the path unit in the current time period from the plurality of vehicles, and taking the average value of the average speeds of all vehicles which pass through the path unit in the current time period as the current average speed of the path unit in the current time period.
5. A detection method according to claim 3, characterised in that said step 3.2 comprises:
step 3.21, respectively aiming at each vehicle in the plurality of vehicles, obtaining the time of the vehicle passing through each path unit in the historical time period by a linear interpolation method, and obtaining the average speed of the vehicle passing through each path unit in the historical time period according to the time of the vehicle passing through each path unit in the historical time period;
and 3.22, respectively aiming at each path unit, determining all vehicles which pass through the path unit in the historical time period from the plurality of vehicles, and taking the average value of the average speeds of all vehicles which pass through the path unit in the historical time period as the historical average speed of the path unit in the historical time period.
6. A detection method according to claim 3, characterised in that said step 3.3 comprises:
step 3.31, respectively aiming at each path unit, passing through a formula
Obtaining the average speed difference of the path unit in the current time period;
wherein,
representing the current average speed of the path element over said current time period,
representing the history of the path unit in the history time periodThe average speed, Δ v, represents the average speed difference of the path unit over the current time period.
7. The detection method according to claim 1, wherein the step 4 comprises:
step 4.1, constructing an adjacency matrix according to the topological relation of each path unit in the road network;
step 4.2, taking the path units with the average speed difference smaller than 0 in each path unit in the road network as candidate seed units;
step 4.3, respectively aiming at each candidate seed unit, executing the following steps:
expanding to the first-order neighborhood according to the topological relation of each path unit in the road network, and calculating the local G of the candidate seed unit and each adjacent path uniti *Index and select the calculated local Gi *Combining the adjacent path unit with the maximum absolute value in the exponent with the candidate seed unit until all the first-order adjacent path units are combined or the calculated local G is obtainedi *Obtaining a merging path until the absolute value of the exponent is not increased;
expanding k-order adjacent path units of the merged path according to the topological relation of each path unit in the road network until the local G of the k-order adjacent path units is calculatedi *Obtaining a candidate path until the absolute value of the index is not increased; wherein k is an integer greater than or equal to 2.
8. The detection method according to claim 7, wherein the local Gi index is calculated by the formula:
wherein, Δ vjRepresenting the difference between the historical average speed and the current average speed of path element j,
representing the average of all average velocity differences within said investigation region, n representing the total number of path elements within said investigation region, wi,jIs the adjacency matrix of path element i and path element j, and s is the variance of the study region.
9. The detection method according to claim 8, wherein s is calculated by the formula:
wherein, Δ vjRepresenting the difference between the historical average speed and the current average speed of path element j,
represents the average of all average velocity differences within the investigation region and n represents the total number of path elements within the investigation region.
10. The detection method according to claim 1, wherein the step 5 comprises:
step 5.1, respectively aiming at each candidate path, executing the following steps:
generating N simulation data sets for the candidate paths;
computing a likelihood ratio statistic score LL R for the candidate paths in each simulated datasetobs；
Through a male
Calculating the significance of the candidate path; wherein, # (f)i) Indicates that the condition f is satisfiediN is the total number of simulation data sets, LL RresScoring a likelihood ratio statistic for said candidate paths in the real data, SiRepresents said candidate path, p _ value (S)i) Representing the significance of the candidate path;
when the significance p _ value of the candidate path (S)i) And when the significance of the candidate route is less than or equal to α, determining that the significance of the candidate route meets a preset significance level, and determining that the candidate route is a sporadic congestion route, wherein α is the preset significance level.
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